Deep learning has accelerated progress in Object Detection research [1]}, [2]}, [3]}, [4]}, [5]}, where a model is tasked to identify and localise objects in an image.
All existing approaches work under a strong assumption that all the classes that are to be detected would be available at training phase. Two challenging scenarios arises when we relax this assumption:
1) A test image might contain objects from unknown classes, which should be classified as unknown.
2) As and when information (labels) about such identified unknowns become available, the model should be able to incrementally learn the new class.
Research in developmental psychology [6]}, [7]} finds out that the ability to identify what one doesn't know, is key in captivating curiosity. Such a curiosity fuels the desire to learn new things [8]}, [9]}.
This motivates us to propose a new problem where a model should be able to identify instances of unknown objects as unknown and subsequently learns to recognise them when training data progressively arrives, in a unified way. We call this problem setting as Open World Object Detection.
